Feedbackless Relaying for Enhancing Reliability of Connected Vehicles

This paper considers the network- and application-level reliabilities for connected vehicles under path loss environments. We derive a probabilistic framework for estimating reliabilities that is applicable with various path loss models. We also build a realistic connected vehicle traffic simulation...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2020-05, Vol.69 (5), p.4621-4634
Main Authors: Ali, G. G. Md. Nawaz, Ayalew, Beshah, Vahidi, Ardalan, Noor-A-Rahim, Md
Format: Article
Language:English
Subjects:
autonomous relaying
Biological system modeling
Computer simulation
Connected vehicles
Engineering
Network reliability
path loss model
Propagation losses
Radio frequency
Relaying
Relays
Reliability
Safety
Telecommunications
Transportation
Vehicle-to-everything
Vehicles
Vehicular networks
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Summary:This paper considers the network- and application-level reliabilities for connected vehicles under path loss environments. We derive a probabilistic framework for estimating reliabilities that is applicable with various path loss models. We also build a realistic connected vehicle traffic simulation environment and use it to perform extensive experiments considering semi-urban traffic. The results show that the achievable reliability performances differ significantly with the path loss models considered. For a moderate communication distance between a transmitter and a receiver, with established deterministic and stochastic path loss models, the network-level reliability is around 55%, whereas with the realistic path loss models that consider obstacles and traffic, the reliability falls below 30%. To improve the network- and application-level reliabilities, we propose a feedbackless relaying mechanism that can be deployed on top of IEEE 802.11p, where, the relay vehicle selection is done autonomously. The relaying mechanism improves the network-level and application-level reliabilities by at least 35% for the studied path loss models.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2020.2980848